FCN中的transplant

FCN中的surgery.transplant函数用于拷贝learnable参数，其直接目的是：将VGG分类模型中的一些全连接层的参数正确地拷贝到相应的目标全连接层中。代码如下：

def transplant(new_net, net, suffix=''):
    """
    Transfer weights by copying matching parameters, coercing parameters of
    incompatible shape, and dropping unmatched parameters.

    The coercion is useful to convert fully connected layers to their
    equivalent convolutional layers, since the weights are the same and only
    the shapes are different.  In particular, equivalent fully connected and
    convolution layers have shapes O x I and O x I x H x W respectively for O
    outputs channels, I input channels, H kernel height, and W kernel width.

    Both  `net` to `new_net` arguments must be instantiated `caffe.Net`s.
    """
    for p in net.params:
        p_new = p + suffix
        if p_new not in new_net.params:
            print 'dropping', p
            continue
        for i in range(len(net.params[p])):
            if i > (len(new_net.params[p_new]) - 1):
                print 'dropping', p, i
                break
            if net.params[p][i].data.shape != new_net.params[p_new][i].data.shape:
                print 'coercing', p, i, 'from', net.params[p][i].data.shape, 'to', new_net.params[p_new][i].data.shape
            else:
                print 'copying', p, ' -> ', p_new, i
            new_net.params[p_new][i].data.flat = net.params[p][i].data.flat

ndarray.flat返回flatiter对象，即

surgery.transplant的调用方式在solve.py中：surgery.transplant(solver.net,vgg_net)

import sys    
sys.path.append('/home/my/caffe-master/caffe-master/python')  
import caffe  
import surgery, score  

import numpy as np  
import os  
import sys  

try:  
    import setproctitle  
    setproctitle.setproctitle(os.path.basename(os.getcwd()))  
except:  
    pass  

vgg_weights = '../ilsvrc-nets/vgg16-fcn.caffemodel'  
vgg_proto = '../ilsvrc-nets/VGG_ILSVRC_16_layers_deploy.prototxt'  
weights = '../ilsvrc-nets/vgg16-fcn.caffemodel'  

# init  
caffe.set_mode_gpu()  
# caffe.set_device(int(sys.argv[0]))  
caffe.set_device(7)  

#solver = caffe.SGDSolver('solver.prototxt')  
#solver.net.copy_from(weights)  
solver = caffe.SGDSolver('solver.prototxt')  
vgg_net=caffe.Net(vgg_proto,vgg_weights,caffe.TRAIN)  
surgery.transplant(solver.net,vgg_net)  
del vgg_net  

# surgeries  
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]  
surgery.interp(solver.net, interp_layers)  

# scoring  
test = np.loadtxt('../data/sift-flow/test.txt', dtype=str)  

for _ in range(50):  
    solver.step(2000)  
    # N.B. metrics on the semantic labels are off b.c. of missing classes;  
    # score manually from the histogram instead for proper evaluation  
    score.seg_tests(solver, False, test, layer='score_sem', gt='sem')  
    score.seg_tests(solver, False, test, layer='score_geo', gt='geo')